Recent Publications and Presentations

OpenGL Data Visualization Cookbook
Lo R, Lo W. OpenGL Data Visualization Cookbook. Birmingham, UK: Packt Publishing; 2015 pp. 298. AmazonAbstract

About This Book

  • Get acquainted with a set of fundamental OpenGL primitives and concepts that enable users to create stunning visuals of arbitrarily complex 2D and 3D datasets for many common applications
  • Explore interactive, real-time visualization of large 2D and 3D datasets or models, including the use of more advanced techniques such as stereoscopic 3D rendering.
  • Create stunning visuals on the latest platforms including mobile phones and state-of-the-art wearable computing devices

What You Will Learn

  • Install, compile, and integrate the OpenGL pipeline into your own project
  • Create interactive applications using GLFW to handle user inputs and the Android Sensor framework to detect gestures and motions on mobile devices
  • Use OpenGL primitives to plot 2-D datasets such as time series dynamically
  • Render complex 3D volumetric datasets with techniques such as data slicers and multiple viewpoint projection
  • Render images, videos, and point cloud data from 3D range-sensing cameras using the OpenGL Shading Language (GLSL)
  • Develop video see-through augmented reality applications on mobile devices with OpenGL ES 3.0 and OpenCV
  • Visualize 3D models with meshes and surfaces using stereoscopic 3D technology

Style and approach

This is an easy-to-follow, comprehensive Cookbook showing readers how to create a variety of real-time, interactive data visualization tools. Each topic is explained in a step-by-step format. A range of hot topics is included, including stereoscopic 3D rendering and data visualization on mobile/wearable platforms.

González G, Parot V, Lo W, Vakoc BJ, Durr NJ. Feature Space Optimization for Virtual Chromoendoscopy Augmented by Topography. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 [Internet]. 2014;8673 :642-649. Publisher's VersionAbstract

Optical colonoscopy is the preferred modality for the screening and prevention of colorectal cancer. Chromoendoscopy can increase lesion detection rate by highlighting tissue topography with a colored dye, but is too time-consuming to be adopted in routine colonoscopy screening. We developed a fast and dye-free technique that generates virtual chromoendoscopy images that incorporate topography features acquired from photometric stereo endoscopy. We demonstrate that virtual chromoendoscopy augmented by topography achieves similar image quality to conventional chromoendoscopy in ex-vivo swine colon.

Lo W, Tang H, Zhang E, Bouma B, Vakoc B. Hybrid FPGA and GPU acceleration of optical frequency domain angiography computation (Oral Presentation). SPIE Photonics West – BiOS 2012. 2012.Abstract

Optical frequency domain imaging (OFDI) has shown promise in numerous clinical and preclinical applications. However, at current data acquisition rates, the OFDI system can only process ~10-20% of the acquired data in real-time on latest-generation CPUs. This is particularly burdensome in angiographic imaging (OFDA) that acquires large datasets and requires extensive processing. To solve this problem, we developed a hybrid computational framework that combines fully pipelined hardware implemented on field programmable gate arrays (FPGAs) with streaming CUDA software implemented on graphics processing units (GPUs). Our design features FPGA-accelerated interferogram processing at 220 MHz and GPU-accelerated angiogram reconstruction at ~182fps.  

Computational Acceleration for Medical Treatment Planning: Monte Carlo Simulation of Light Therapies Accelerated using GPUs and FPGAs
Lo W, Rose J, Lilge L. Computational Acceleration for Medical Treatment Planning: Monte Carlo Simulation of Light Therapies Accelerated using GPUs and FPGAs. 1st ed. VDM Verlag; 2010. AmazonAbstract

In medicine, there is a clear trend towards individualized therapies, for cancer and other diseases. Individualized treatment planning for cancer, particularly in radiotherapy and light therapies, is a complex optimization problem. As analytical inverse planning solutions do not exist for light therapies, a large number of light delivery configurations must be evaluated to find one that best conforms to the clinical target (e.g., a tumour). An integral part of this optimization is the accurate computation of light dose, ideally using Monte Carlo (MC) simulations for realistic, 3-D modelling. This text explores two hardware-accelerated solutions to overcome the general speed limitation of MC simulations: (1) designing custom hardware on field-programmable gate arrays, and (2) creating highly parallel software on graphics processing units (GPUs). Notably, a speedup of over 1000x was achieved on four GPUs compared to a state-of-the-art CPU. As the Monte Carlo method is used in many fields such as radiation medicine, this text also includes the GPU MC code package and is of interest to scientists, engineers, and medical professionals exploring real-time treatment planning solutions.

Alerstam *E, Lo *W, Han TD, Rose J, Andersson-Engels S, Lilge L. Next-generation acceleration and code optimization for light transport in turbid media using GPUs (*Co-first authors). Biomedical Optics Express [Internet]. 2010;1 (2) :658-675. Publisher's VersionAbstract

A highly optimized Monte Carlo (MC) code package for simulating light transport is developed on the latest graphics processing unit (GPU) built for general-purpose computing from NVIDIA - the Fermi GPU. In biomedical optics, the MC method is the gold standard approach for simulating light transport in biological tissue, both due to its accuracy and its flexibility in modelling realistic, heterogeneous tissue geometry in 3-D. However, the widespread use of MC simulations in inverse problems, such as treatment planning for PDT, is limited by their long computation time. Despite its parallel nature, optimizing MC code on the GPU has been shown to be a challenge, particularly when the sharing of simulation result matrices among many parallel threads demands the frequent use of atomic instructions to access the slow GPU global memory. This paper proposes an optimization scheme that utilizes the fast shared memory to resolve the performance bottleneck caused by atomic access, and discusses numerous other optimization techniques needed to harness the full potential of the GPU. Using these techniques, a widely accepted MC code package in biophotonics, called MCML, was successfully accelerated on a Fermi GPU by approximately 600x compared to a state-of-the-art Intel Core i7 CPU. A skin model consisting of 7 layers was used as the standard simulation geometry. To demonstrate the possibility of GPU cluster computing, the same GPU code was executed on four GPUs, showing a linear improvement in performance with an increasing number of GPUs. The GPU-based MCML code package, named GPU-MCML, is compatible with a wide range of graphics cards and is released as an open-source software in two versions: an optimized version tuned for high performance and a simplified version for beginners (http://code.google.com/p/gpumcml).

Lo W, Han D, Rose J, Lilge L. GPU-accelerated Monte Carlo simulation for photodynamic therapy treatment planning (Oral Presentation). European Conferences on Biomedical Optics (ECBO 2009) [Internet]. 2009. Publisher's VersionAbstract

Recent improvements in the computing power and programmability of graphics processing units (GPUs) have enabled the possibility of using GPUs for the acceleration of scientific applications, including time-consuming simulations in biomedical optics. This paper describes the acceleration of a standard code for the Monte Carlo (MC) simulation of photons on GPUs. A faster means for performing MC simulations would enable the use of MC-based models for light dose computation in iterative optimization problems such as PDT treatment planning. We describe the computation and how it is mapped onto the many parallel computational units now available on the NVIDIA GTX 200 series GPUs. For a 5 layer skin model simulation, a speedup of 277x was achieved on a single GTX280 GPU over the code executed on an Intel Xeon 5160 processor using 1 CPU core. This approach can be scaled by employing multiple GPUs in a single computer - a 1052x speedup was obtained using 4 GPUs for the same simulation.

Lo W, Redmond K, Luu J, Chow P, Rose J, Lilge L. Hardware acceleration of a Monte Carlo simulation for photodynamic therapy treatment planning. Journal of Biomedical Optics [Internet]. 2009;14 (1) :014019. Publisher's VersionAbstract

Monte Carlo (MC) simulations are being used extensively in the field of medical biophysics, particularly for modeling light propagation in tissues. The high computation time for MC limits its use to solving only the forward solutions for a given source geometry, emission profile, and optical interaction coefficients of the tissue. However, applications such as photodynamic therapy treatment planning or image reconstruction in diffuse optical tomography require solving the inverse problem given a desired dose distribution or absorber distribution, respectively. A faster means for performing MC simulations would enable the use of MC-based models for accomplishing such tasks. To explore this possibility, a digital hardware implementation of a MC simulation based on the Monte Carlo for Multi-Layered media (MCML) software was implemented on a development platform with multiple field-programmable gate arrays (FPGAs). The hardware performed the MC simulation on average 80 times faster and was 45 times more energy efficient than the MCML software executed on a 3-GHz Intel Xeon processor. The resulting isofluence lines closely matched those produced by MCML in software, diverging by only less than 0.1 mm for fluence levels as low as 0.00001cm^−2 in a skin model.

  • «
  • 2 of 2
  •  
More